A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
The use of explicit user models in text generation: tailoring to a user's level of expertise
The use of explicit user models in text generation: tailoring to a user's level of expertise
Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Explanations in Knowledge Systems: The Roles of the Task Structure and Domain Functional Models
IEEE Expert: Intelligent Systems and Their Applications
Generating causal explanation from a cardio-vascular simulation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Computational and physical causality
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Scaling up self-explanatory simulators polynomial time compilation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Generating explanations of device behavior is a long-standmg goal of AI research in reasoning about physical systems. Much of the relevant work has concentrated on new methods for modeling and simulation, such as qualitative physics, or on sophisticated natural language generation, in which the device models are specially crafted for explanatory purposes. We show how two techniques from the modeling research--compositional modeling and causal ordering-- can be effectively combined to generate natural language explanations of device behavior from engineering models. The explanations offer three advances over the data displays produced by conventional simulation software: (1) causal interpretations of the data, (2) summaries at appropriate levels of abstraction (physical mechanisms and component operating modes), and (3) query-driven, natural language summaries. Furthermore, combining the compositional modeling and causal ordering techniques allows models that are more scalable and less brittle than models designed solely for explanation. However, these techniques produce models with detail that can be distracting in explanations and would be removed in hand-crafted models (e.g., intermediate variables). We present domain-independent filtering and aggregation techniques that overcome these problems.